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Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

Coastal Santa Barbara County, California is characterized by extreme downslope wind events on the southern side of the Santa Ynez Mountains known as Sundowner winds (or Sundowners) that can rapidly spread wildfires toward highly populated regions. This research investigates these strong winds and the risk of wildfire spread in this region. The second chapter of this research explores the seasonal and diurnal patterns of wind, temperature, dew point, and a fire weather index, as well as their spatiotemporal variability, from an observational perspective. Utilizing station and buoy data, distinct differences were found between stations on the mountain slopes and stations on the foothills and on the coastal plain. Additionally, correlations revealed relationships between stations and buoys, and analysis of a vertical wind profiler at the Santa Barbara airport provided evidence of differences during Sundowners in the east and west parts of coastal Santa Barbara. Due to the dependence on winds in its calculation, fire weather indices were highest at stations on the mountain slopes, though all stations had recorded at least one time with critical fire weather. In the third chapter, the ability of an operationally-used uncoupled wildfire model called FARSITE (Fire Area Simulator) in reconstructing two wildfires that rapidly spread by Sundowner winds was tested. One wildfire was simulated relatively well, though some differences in spread direction were evident. The perimeter and burn area of the second wildfire was underestimated in the simulations due to limitations built into the spotting algorithm. It was determined that the elevation of the launch site of an ember must be lower than the landing site. In the simulations for the Santa Barbara case studies, fire spread was predominantly downslope, thus the launch sites were commonly higher in elevation and spotting did not occur. This finding has ramifications for wildfires that are rapidly spread by spotting ahead of the fire front. In the fourth chapter, I created wildfire risk maps for this high-risk region. Three ignition modeling methods were employed using distances from previous ignitions and roads and trails, and these were input into the wildfire model with three varying wind scenarios: climatological winds, a composite of Sundowners, and a case study of an extreme Sundowner event. Simulations commonly hit areas on the mountain slopes overlooking downtown Santa Barbara and Montecito, and also near a major north-south running evacuation route, in part due to clustering of ignitions in this region. The simulations using climatological winds spread generally outward from the ignition locations and in contrast simulations run using extreme winds spread further south into the wildland-urban interface and toward more urban areas. These three chapters advance knowledge of typical versus extreme winds in this region, demonstrate how simulations align with previous influential wildfires, provide insight into wildfire model limitations, and identify potentially high-risk regions for wildfires, increasing wildfire resilience and preparedness.

Accurately simulating snow cover dynamics and the snow–atmosphere coupling is of major importance for topics as wide-ranging as water resources, natural hazards, and climate change impacts with consequences for sea level rise. We present a new modelling framework for atmospheric flow simulations for cryospheric regions called CRYOWRF. CRYOWRF couples the state-of-the-art and widely used atmospheric model WRF (the Weather Research and Forecasting model) with the detailed snow cover model SNOWPACK. CRYOWRF makes it feasible to simulate the dynamics of a large number of snow layers governed by grain-scale prognostic variables with online coupling to the atmosphere for multiscale simulations from the synoptic to the turbulent scales. Additionally, a new blowing snow scheme is introduced in CRYOWRF and is discussed in detail. CRYOWRF's technical design goals and model capabilities are described, and the performance costs are shown to compare favourably with existing land surface schemes. Three case studies showcasing envisaged use cases for CRYOWRF for polar ice sheets and alpine snowpacks are provided to equip potential users with templates for their research. Finally, the future roadmap for CRYOWRF's development and usage is discussed.

The main purpose of this paper is to observe the effect PV variation of solar temperature and irradiance on different conditions and on the inverter output for a grid-connected system. Majorly temperature& solar irradiation effects the performance of a grid connected inverter, also on the photo-voltaic (PV) electric system. The simulation based study was carried out in order to evaluate the variation of inverter output with the variation of solar temperature and irradiance with the variation in climate. The analysis of Grid-connected inverter and their performance at various seasons and conditions is investigated. Solar power plant for a year. In solar power plant efficiency of inverter is also considered to calculate overall losses so, the inverter efficiency and plant performance are considered in this paper using MAT Lab software. In summer season the inverter performed efficiency is decreased because of peak temperature value and slightly increased with the increase in irradiance.

Forest fires are very intense deadly destroying the homes, wildlife, timber also polluting the atmosphere with hazardous compounds of pollutants. Forest fire produces various ill effects and increases the global temperature; it has a prolonged impact on landscapes and deduces the production of oxygen as it destroys past spreading. As wildfire is a part of nature its intensity cannot be handled after crossing a threshold level. The proposed system detects the forest fire at minimal stage with assistance of cameras fixed at the towers. With real time fire detection algorithm programmed, fire alerts the respective forest department with unique node set location with Long Range network (LoRa). Proposed Fire detection system is placed at the end of node networks with gateway connected and intern with interneted to local forest control areas. The fire detection system consumes very low power as they are energised with localised solar systems. Forest ranges for long dimensions therefore these LoRa can transmit data to a larger range.

Spatially explicit quantification on design storms are essential for flood risk assessment and planning. Since the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations having a substantially long time coverage. To achieve a regional picture these station based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random short duration events, 15 minute) to a more structured, orographically influenced pattern (long duration events, 24 hours) seems to be much more realistic in the weather radar based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

An indoor thermal environment is affected by various heat elements, such as heat transfer through walls, solar radiation, and heat emissions from people, lighting and equipment. To promote both local thermal comfort and building energy efficiency, demand-oriented ventilation (such as personalized ventilation) has been developed. When using this method, a good understanding on indoor temperature distribution becomes necessary. For this purpose, an index known as Contribution Ratio of Indoor Climate (CRI) has been developed through extraction from the calculation results of Computational Fluid Dynamics (CFD). This index can be used to analyze the independent contribution of each heat element to indoor temperature distribution. In this paper, a complete and detailed introduction of the CRI is given, including its basic premises, definitions, and mathematical meaning. Particularly, calculation method of the CRI in natural convection airflow fields is further developed. Two cases (forced and natural convection airflow fields) have been carried out in different scenarios, with results showing that the CRI of a heat source had higher values in the area around itself. Also, it had a larger influence range in forced convection airflow field because of the convective airflow, while relatively larger CRI values only appear in the area above the heat source in the natural convection airflow field because of the heat plume. As a useful index for understanding the form of indoor temperature field, the CRI has guiding significance for regulating air-conditioning/ventilation systems to build better indoor thermal environment.

There are prototypes of social housing massively built in contrasting climatic conditions, generating thermal comfort needs that are difficult to satisfy by the users themselves. Variation of indoor air volume in living spaces where the use of air conditioning and natural ventilation strategies provides elements to improve thermal comfort conditions. This research shows the thermal performance located in a representative social housing according to Mexico's National Housing Commission. Operative temperature results from a benchmark case were compared to six Virtual Evaluation Models, using the Dynamic Thermal Simulation tool Design Builder® from the perspective of probability. The main objective was to determine the minimum use of active systems with different indoor air volumes and improve comfort conditions to promote sustainable living in social housing. The analysis was conducted under an adaptive comfort range according to three different climate conditions in Mexico adopting a Numerical Theoretical Method. The main findings can be divided into two parts: a) the impact of the indoor air volume on thermal performance was evidenced in a proportion of time in three representative climates of the central region of Mexico, and b) no relationship was found between indoor air volume and thermal comfort in sub-humid cold climate; in sub-humid temperate climate, the same number of comfort hours was found in two different models, and in sub-humid warm climate, an inversely proportional relationship was found between indoor air volume and the comfort hours. This findings implies a greater knowledge relative to what we know about sub-humid cold, temperate and warm climates.

Rüzgar enerjisinin değerlendirilmesi, herhangi bir yerde bulunan potansiyel rüzgar enerjisi hakkında uzmanları bilgilendiren entegre analizlerin bir biçimidir. Bu tür değerlendirmenin başlangıç noktası, belirli bir yerde yaygın olarak meydana gelen rüzgar modellerini anlamaktır. Bunu, rüzgar verileri toplama ve toplanan verileri analiz etme izler. Bu makale, birinci ve ikinci bölgelerin kapsamlı bir çalışmasıdır. Bu nedenle, ortalama rüzgar hızı, yön verisi, kısa vadeli varyasyonlar (veya rüzgarlar), yıllık, mevsimsel ve hatta günlük değişiklikler / varyasyonlar ve yüksekliğe bağlı varyasyonlar gibi birkaç parametre dikkate alınmalıdır. Bahsedilen tüm parametreler sahaya özgü olduğundan, uzun bir süre boyunca yerinde ölçümlerle doğru bir şekilde belirlenebilir. Bu çalışma, rüzgar değerlendirmesi ve potansiyel rüzgar enerjisi üzerine odaklandığından Gharyan şehri bir konum olarak seçildi ve alt konumları Gharyan 1, 2, 3 ve 4 olarak adlandırıldı. İlk olarak, rüzgar enerjisi kaynağı ile ilgili konumun yakınında bulunan meteoroloji istasyonlarından alınan veriler arka planda toplandı. Genellikle, her bir konum için 3 saatlik uzun vadeli rüzgar verileri kullanılır ve daha sonra, göbek yüksekliğindeki gerçek rüzgar hızını bulmak için rüzgar verilerinin yeniden hesaplanması yapılır. Önemli hesaplama parametreleri arasında, önerilen her bir saha için hesaplanması gereken Weibull dağılımı, ortalama rüzgar hızı, yıllık kapasite faktörü ve yıllık enerji faktörü yer alır. Yıllık kapasite ve enerji faktörlerinin hesaplanması, genellikle Vestas (V60, 850kW) olarak adlandırılan rüzgar türbinini seçmek için gereklidir. Bu makale, Libya'daki bazı tesislerin, özellikle yıllık enerji, kapasite faktörü ve kabul edilebilir enerji üretim kapasitesinin kaydedildiği Gharyan'da bulunan dört bölgede yeterli rüzgar enerjisine sahip olduğunu açıkça göstermektedir., Assessing the potential of wind energy involves an integrated analysis approach, the conclusions of which inform the experts of the field about the possibilities of harnessing wind energy on a certain location. This type of analysis is made of multiple steps, starting from observing and understanding commonly occurring wind patterns at the designated site, followed by collection of data and finally analysis. The current paper is an extended study on the first two steps where several parameters such as wind’s average speed and its direction, as well as various changes such as short-term ones (or gusts), yearly, seasonal, and even daily changes, and height-related variations were collected. Since the above-mentioned parameters are site-specific, they can be accurately determined through on-site measurements during extended time periods. The site for this study is the city of Gharyan in Libya which was divided into four sub-locations. Data were gathered from meteorological stations located in the vicinity of the designated area in three-hours periods, and they were then used to recalculate wind’s real speed and height at a specific hub. Among the important calculation parameters were Weibull's distribution, average wind speed, annual capacity factor, and the annual energy factor, all of them required for each proposed site. The calculations of the annual capacity and energy factors are necessary for choosing the appropriate wind turbine, generally named Vestas (V60, 850kW). The current study clearly shows that some sites, in particular four locations in Ghayran where feasible annual energy, capacity factor, and acceptable power generation capacity were recorded have wind energy for civilian purposes.

This paper presents the results of thermal comfort and air quality studies in naturally ventilated residential buildings in Kampala City, Uganda. Questionnaire surveys were used for obtaining occupant subjective thermal sensation votes. Indoor and outdoor measurements for air temperature (Ta), Mean Radiant Temperature (MRT), relative humidity, air speed, and air quality were done for seven buildings over fifteen days during the month of June 2019. DesignBuilder software was used to develop the reference building model and to simulate strategies for improvement of thermal comfort. Survey results showed that 67.8% of the respondents were comfortable with the indoor thermal environment. The indoor air temperature range was 22.7°C - 27.9°C which lies within the 80% acceptable limits set by ASHRAE 55. The measured indoor MRT range was 24.24°C - 25.57°C. Measured levels of indoor PM2.5 were double the World Health Organization recommended limits, while the levels of PM10 were thrice the recommended limits of ASHRAE 62.1. The CO2 concentrations were within the limits set by ASHRAE 62.1. The developed model predicted comfortable indoor conditions with a temperature range of 23.6°C - 25.3°C based on the 80% acceptable limits set by ASHRAE 55. The results show that majority of occupants preferred cooler temperatures during the day which is justified by the use of adaptive measures to obtain thermal comfort.

Thermal comfort inside classrooms located in an institutional building is examined as a function of air temperature, relative humidity and wind velocity. The effect of east-west orientation on the thermal comfort inside the classrooms located in different floor levels is understood based on field experiments and validated using computational fluid dynamics simulations. The air temperature inside the classrooms shows an increasing trend with the floor level. Air temperature and relative humidity inside the classrooms reveal an inverse relationship as a function of east-west orientation with different floor level. As time progresses, the air temperature inside classrooms located on the eastern side of the building is apparently higher than rooms situated on the western side of the building, both field experiments and simulation acknowledge the same. Appropriate reasons have been proposed behind the discussed phenomenon using computational fluid dynamics simulation.

The pixel size of the Infrared Atmospheric Sounding Interferometer (IASI) remote sensor is much smaller than the horizontal grid size of current Chemical Transport Models (CTMs). In order to assimilate the maximum of information from the IASI retrievals, we have increased the horizontal resolution of our model MOCAGE to be consistent with the IASI pixel size. Experiments are carried out with the Valentina data assimilation system using the standard and the high resolution versions of the model. Two resolutions of the horizontal Gaussian grid have been used for the model: with a T42 and a T170 triangular truncations. Our study is based on the combination of data from the IASI instrument and from the Microwave Limb Sounder (MLS), since this latter dataset allows the information to be spread through the whole atmospheric columns at a low computational cost. Two datasets of ozone super-observations have been constructed by averaging the IASI data on the two model grids. Direct model simulations without data assimilation first show that the increase of the horizontal resolution modifies the ozone smallest scale structures as well as the ozone meridional distribution. This modification results from a better representation of the vertical velocity with the T170 configuration. When the ozone assimilation is performed there is less influence of the horizontal resolution of the model. Nevertheless, in a general way, comparisons with independent data show large reductions of the ozone standard deviations when the resolution is increased. When the ozone assimilation is performed with the high resolution dataset, the high resolution model does not improve the ozone analysis compared to the one obtained with the same model resolution but with the low resolution IASI dataset. This result is due to the difficulty to combine IASI data and MLS data. For assimilating IASI data at high resolution the horizontal correlation length-scale has to be decreased to catch the small scale structures present in the dataset. By doing so the influence of the coarser resolution MLS data is decreased and part of the information brought on the vertical shape of the ozone profile is lost. It is concluded that it is essential to add information on the vertical distribution of ozone column when the IASI data is assimilated at a resolution close to the pixel size. Using IASI averaging kernels would likely improve the simulations, but the computational cost would be much higher. Alternatively, better results might be obtained by a careful tuning of the horizontal correlation length-scale.

International audience; Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 \unit{km} horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to -5 % for Paris and -2 % for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between -10 and -5 % while for Stockholm we observe mixed trends up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily average and maximum ozone and 20 % for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective.

Twenty-one cases of boundary-layer (BL) structure were retrieved by three co-located remote sensors, one lidar (Leosphere ALS300) and two ceilometers (Vaisala CL31, Jenoptik CHM15K). Data were collected during the ICOS field campaign held at the GAW Atmospheric Station of Mace Head, Ireland, from 8 to 28 June 2009. The study is a two-step investigation of the BL structure based (i) on the intercomparison of backscatter profiles from the three laser sensors and (ii) on the comparison of the backscatter profiles with twenty-three radiosoundings performed during the period of 8 to 15 June 2009. The Temporal Height-Tracking (THT) algorithm was applied to the three sensors' backscatter profiles to retrieve the decoupled structure of the BL over Mace Head. The results of the intercomparisons are expressed in terms of the mean correlation coefficients, mean bias (difference between two sensors' detections), mean sigma (the standard deviation of the bias) and the consistency, i.e. the percentage of cases where the detections of the intercompared sensors were closer than 200 m. The ALS300-CHM15K comparison provided the most consistent retrievals amongst the three comparisons with, respectively, the 86.5% and 77.2% of the lower and upper layer detections closer than 200 m and with correlation coefficients equal to 0.88 and 0.83 at the lower and upper layer, respectively. The lidar and ceilometers-detected BL heights were then compared to the temperature profiles retrieved by radiosoundings. The most consistent retrievals at the lower layer are from the ALS300 with the 75% of detections closer than 200 m to the radiosoundings' first temperature inversion. Despite the lower signal-to-noise ratio and R-value compared to the ASL300 and CHM15K, the CL31 is more consistent with the radiosoundings retrievals at the upper layer with 62.5% of detections closer than 200 m to the radiosoundings' second temperature inversion. The ALS300 has larger pulse-averaged power compared to the two ceilometers and better ability in detecting fine aerosol layers within the BL. The comparison of remote and in-situ data proved both the veracity of the inherent link between temperature and aerosol backscatter profiles, and the existence of possible limitations in using aerosols as a tracer to detect the BL structure.

Around the world people are rapidly moving to cities. The rapid urbanization has led to changes in land use and land cover, which (i) modify the urban surface energy balance making the cities hotter than the surrounding rural area, (ii) exacerbate the impact of extreme weather events like heatwaves and (iii) lead to poor quality of life. City administrations around the world are undertaking drastic measures to mitigate extreme heat. Here, in this study we test a wearable sensing platform to both study pedestrian-level microclimate boundary conditions as well as the impact of urban greening on moderating excess heat in dense urban areas. The experiment presented here was conducted in New York City. Typically, the urban thermal state is estimated by means of direct observations from ground-based sensors, satellite based remote sensing techniques, and high-resolution urban climate modeling, all of which are too coarse to resolve pedestrian-level impacts, a key parameter in determining heat stress for citizens occupying the outdoors. Here a wearable and a portable sensing apparata were used to monitor key environmental parameters - air temperature, relative humidity, wind speed, mean radiant temperature and solar radiation. The coupled monitoring platform included multiple sensors and a wireless data logging system which were all prototyped by the authors on open source technologies. The sensor platform was able to accurately map the thermal environment of multiple dense urban spaces, being to reproduce the spatial variability in key microclimate parameters, and the performance was comparable to traditional stationary ground-based weather stations. The results, which may be of key help also for validating microclimate forecasting models, indicate high spatial variability in temperature, humidity and solar radiation within the same urban parks. Our findings also indicate that on average small urban parks in a dense urban setting were able to reduce the air temperature by 3–7 °C in New York City with major gains during the mid-afternoon periods.

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to forecasts of the day-ahead maximum value of ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use-case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighborhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions. Our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps to increase the accuracy of the forecasts for specific ranges of ozone’s daily peak values.

Compound hazards refer to two or more different natural hazards occurring over the same time period and spatial area. Compound hazards can operate on different spatial and temporal scales than their component single hazards. This article proposes a definition of compound hazards in space and time, presents a methodology for the spatiotemporal identification of compound hazards (SI–CH), and compiles two compound-hazard-related open-access databases for extreme precipitation and wind in Great Britain over a 40-year period. The SI–CH methodology is applied to hourly precipitation and wind gust values for 1979–2019 from climate reanalysis (ERA5) within a region including Great Britain and the British Channel. Extreme values (above the 99 % quantile) of precipitation and wind gust are clustered with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, creating clusters for precipitation and wind gusts. Compound hazard clusters that correspond to the spatial overlap of single hazard clusters during the aggregated duration of the two hazards are then identified. We compile these clusters into a detailed and comprehensive ERA5 Hazard Clusters Database 1979–2019 (given in the Supplement), which consists of 18 086 precipitation clusters, 6190 wind clusters, and 4555 compound hazard clusters for 1979–2019 in Great Britain. The methodology's ability to identify extreme precipitation and wind events is assessed with a catalogue of 157 significant events (96 extreme precipitation and 61 extreme wind events) in Great Britain over the period 1979–2019 (also given in the Supplement). We find good agreement between the SI–CH outputs and the catalogue with an overall hit rate (ratio between the number of joint events and the total number of events) of 93.7 %. The spatial variation of hazard intensity within wind, precipitation, and compound hazard clusters is then visualised and analysed. The study finds that the SI–CH approach (given as R code in the Supplement) can accurately identify single and compound hazard events and represent spatial and temporal properties of these events. We find that compound wind and precipitation extremes, despite occurring on smaller scales than single extremes, can occur on large scales in Great Britain with a decreasing spatial scale when the combined intensity of the hazards increases.

The global energy demand is growing substantially. Clean and secure energy supply is a must for our civilization's sustainable development.Solar and wind energy is growing fast and can contribute significantly to meet the goals set by many countries to reduce greenhouse gas emissions. A deep and wide investigation of the environmental impact of solar and wind energy is important before any solar or wind plants' construction is made. In this study, the literature is reviewed to summarize theenvironmental impact of solar and wind energy systems in terms of the following factors; land use, water consumption, impact on biodiversity, visual and noise effects, health issues, and impact on micro climate. Although the benefits of solar and wind energy are obvious and great, negative perception of these technologies can inhibit their wide penetration in some regions. This review paper includes a critical and an inclusive analysis of solar and wind energy’s environmental impact and may serve as an important tool to conduct a proper environmental impact assessment. This critical analysis may serve also as a tool for developers, policy, and decision-when planning future solar and wind farms.&nbsp

Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to 6 h, which is a relevant lead time for emergency services in times of crisis. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occurred in southeastern France. Precipitation forecasts are evaluated at a 15- min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrological point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15 min and 1 h 30 min of forecast, the performance of PIAF was higher than AROME-NWC. For longer lead times (up to 3 h) their performances were generally equivalent. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash flood simulations and driven by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash flood forecasting in terms of peak intensity, timing, and first wave of discharge, with an anticipation of these phenomena that can reach several hours.

Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes. Within an idealized simulation framework, these rainfall events are used as input to an operational distributed hydrologic model to evaluate the sensitivity of the hydrologic response to different forcing grid spacings. We consider the finest scale (i.e., 250 m) as reference, and compute event peak flows and volumes across a wide range of basin sizes. We find that the use of increasingly-coarser inputs leads to changes in the distribution of event peak flows and volumes, with the strongest sensitivity at the smallest catchment sizes. Overall, we find that 4-km rainfall simulations represent a good compromise between computational costs and hydrologic performance, providing basic information for future endeavors geared towards regional downscaling.

Volcanic ash advisories are produced by specialised forecasters who combine several sources of observational data and volcanic ash dispersion model outputs based on their subjective expertise. These advisories are used by the aviation industry to make decisions about where it is safe to fly. However, both observations and dispersion model simulations are subject to various sources of uncertainties that are not represented in operational forecasts. Quantification and communication of these uncertainties are fundamental for making more informed decisions. Here, we develop a data assimilation technique which combines satellite retrievals and volcanic ash transport and dispersion model (VATDM) output, considering uncertainties in both data sources. The methodology is applied to a case study of the 2019 Raikoke eruption. To represent uncertainty in the VATDM output, 1000 simulations are performed by simultaneously perturbing the eruption source parameters, meteorology and internal model parameters (known as the prior ensemble). The ensemble members are filtered, based on their level of agreement with Himawari satellite retrievals of ash column loading, to produce a posterior ensemble that is constrained by the satellite data and its uncertainty. For the Raikoke eruption, filtering the ensemble skews the values of mass eruption rate towards the lower values within the wider parameters ranges initially used in the prior ensemble (mean reduces from 1 Tg h−1 to 0.1 Tg h−1). Furthermore, including satellite observations from subsequent times increasingly constrains the posterior ensemble. These results suggest that the prior ensemble leads to an overestimate of both the magnitude and uncertainty in ash column loadings. Based on the prior ensemble, flight operations would have been severely disrupted over the Pacific Ocean. Using the constrained posterior ensemble, the regions where the risk is overestimated are reduced potentially resulting in fewer flight disruptions. The data assimilation methodology developed in this paper is easily generalisable to other short duration eruptions and to other VATDMs and retrievals of ash from other satellites.

The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 μg m−3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 μg m−3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively., Graphical abstract Image, graphical abstract

Time and intensity-specific very short-term forecasting or nowcasting is the biggest challenge faced by an aviation meteorologist. Ground-based microwave radiometer (MWR) has been used for nowcasting convective activity and it was established that there is a good comparison between thermodynamic parameters derived from MWR and GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. In this study, efforts have been made to bring out the efficacy of MWR in nowcasting thunderstorms and fog. Firstly, the observations of MWR located at Palam, New Delhi, India have been compared with the nearest radiosonde data to ascertain the variation in respective profiles. Large differences were found in relative humidity (RH), whereas temperatures from MWR were found to be close to radiosonde observed temperature up to 3.5 km. Subsequently, the scatter plots and correlation coefficients of thermodynamic indices/parameters indicated that most of the parameters are either not correlated or have moderate correlation only for 12:00 UTC profiles. The superepoch technique of lagged composite for various thermodynamic indices/parameters to obtain a combined picture of all the thunderstorm and dense fog cases on the time series could not determine any pattern to predict thunderstorm and dense fog with lead time of 2-4 hours. MWR profile for a case of occurrence of thunderstorm was analyzed. No significant variation was observed in most of the indices (as calculated from MWR observed parameters) prior to the occurrence of thunderstorm. RH at freezing level and between 950 and 700 hPa levels were the only parameters, which increased four hours prior to the occurrence.

High-sided vehicles are particularly vulnerable to high wind conditions and at sites that are regarded as vulnerable, a range of vehicle restrictions are imposed in high winds. These may include vehicle speed reductions or complete restrictions on the movement of different categories of vehicle at different wind gust speeds. This paper builds on earlier work that has been carried out and seeks to develop a simple but conservative method that can be used to specify vehicle restriction strategies. This is based on a collation of a wide range of data for aerodynamic rolling moment coefficients that allows a simple parameterisation to be developed. This is then used in an overturning model to develop a non-dimensional relationship between overturning gust speed and vehicle speed. The parameter used in the non-dimensionalisation is a characteristic wind speed that is a function of vehicle weight and geometry, and effectively specifies the vulnerability of the vehicle to overturning in high winds. Dimensional relationships between overturning gust velocities and vehicle velocities can thus be derived for different vehicle types and used to develop site-specific vehicle restriction methods.

Earth Air Tunnel Heat Exchanger (EATHE) systems are not so much popular in India because of restricted accessibility of ground space in majority of residential and commercial sector. In the present article, thermal performance of newly designed helical shaped EATHE system has been compared with conventional U–shaped EATHE system by keeping similar geometrical and operational configuration for the summer (April–June) and winter (December–February), respectively. In this regard, therefore, numerical model of both EATHE systems have been designed and experimentally validated. The thermal performance of both EATHE systems has been compared in terms of outlet air temperature and effectiveness. The present numerical study suggested that the newly designed helical EATHE system exhibits better thermal performance with lower ground requirement as compared to conventional U-shaped EATHE system. To evaluate the thermal comfort potential of EATHE system in office buildings of India, composite climate specific adaptive thermal comfort boundaries has been used. The detailed thermal performance behaviour of the EATHE system suggests that an additional 80% of the time; ambient air conditions became thermally comfortable during peak summer season. In addition, analysis of the results revealed that EATHE system has more potential during heating operation mode to maintain indoor comfort in winter season of composite climate in India.

According to the few researches on Nearly zero energy residential buildings (NZERB) in hot-summer and cold-winter zone, although it could reduce the cooling load of buildings due to its high thermal insulation and air tightness, it still needed for certain cooling in summer. This paper studied indoor environment of NZERB under three kinds of air-conditioners (split-type air-conditioner, multi-line air-conditioner and ceiling radiant air-conditioner). Firstly, a simulation model of NZERB was established based on Nanjing, a typical city in hot-summer and cold-winter zone. Secondly, variation of indoor air temperature and building load characteristics with outdoor air temperature were studied. Thirdly, indoor environment and energy consumption under three selected conventional air-conditioners in summer were simulated. Finally, the discussion was given, and an air-conditioner combining with convective and radiant cooling were proposed. The results indicated that the air-conditioner needed to be turned on in NZERB in hot-summer and cold-winter zone due to the room air temperature in off-air condition ranged from 32 °C to 36 °C, which was higher than designed indoor environment temperature in summer, but the indoor environment of NZERB under three selected conventional air-conditioners could not meet the requirements of energy saving and comfort at the same time, and a proposed convective-radiant air-conditioner could be fast, stable, and energy saving. The findings can provide a reference for conducting active technology in NZERB.

The cyclone separatoris an important separation device. This paper presents a new type of embedded two-stage cyclone, which includes a 2nd-stage cyclone (internal traditional cyclone) with multiple inlets and a 1st-stage cyclone (outer cylinder) that unifies the 2nd-stage cyclone inlets into one inlet. The Taguchi experimental method was used to studythe two-stage cyclone separator's inlet area on its performance. Studies have shown that the increase of the 1st-stage cyclone inlet area and the increase in the number of 2nd-stage cyclone inlets (N) positively affect reducing the pressure drop and a negative effect on efficiency. It is recommended to use 2S (the original 1st-stage cyclone inlet area) of the 1st-stage cyclone inlet area and 2N of the 2nd-stage cyclone inlets when separating fine particles. Compared with a traditional cyclone, the pressure drop is reduced by 1303 Pa, the mass separation efficiency (Eq) is increased by 0.56%, and the number separation efficiency (En) is increased by 2.05%. When separating larger particles, it is recommended to use 2S of the 1st-stage cyclone inlet area and 4N of the 2nd-stage cyclone inlets. Compared with a traditional cyclone, although En decreases slightly, the pressure drop is reduced by 3055 Pa, and the Eq is increased by 0.56%. The research results provide new insight into the design of the cyclone.

Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

Global installed wind power capacity has risen nearly 4.3 times in the last decade, from 120.7 GW in 2008 to more than 591 GW in 2018. On the other hand, installed wind power capacity in Turkey was reported as 7.37 GW in 2018, and it is scheduled to reach 12 GW in 2023. The aim of this paper is to assess the recent growth of wind power generation in Turkey in terms of power generation technologies, wind power potential, techno-economic feasibility, and visibility of onshore wind turbines. In this respect, several metrics such as cumulative installed wind power capacity (MW), total number of turbines, total swept area of turbines (km2), total hub height of turbines (km), number of the turbine per turbine power capacity (1/GW), swept area per turbine power capacity (m2/MW) and hub height per turbine power capacity (m/MW) are developed to assess wind power generation regionally between the years of 2010 and 2018. Results show that wind power generation capacity is on the rise in Turkey. But this growth also implies an increase in the number and size of turbines. Eventually, turbines with higher hubs and rotor diameters have become more abundant and visible in landscapes.

To forecast tropical cyclone (TC) intensity and structure changes with fidelity, numerical weather prediction models must be “high definition,” i.e., horizontal grid spacing ≤ 3 km, so that they permit clouds and convection and resolve sharp gradients of momentum and moisture in the eyewall and rainbands. Storm-following nests are computationally efficient at fine resolutions, providing a practical approach to improve TC intensity forecasts. Under the Hurricane Forecast Improvement Project, the operational Hurricane Weather Research and Forecasting (HWRF) system was developed to include telescopic, storm-following nests for a single TC per model integration. Subsequently, HWRF evolved into a state-of-the-art tool for TC predictions around the globe, although its single-storm nesting approach does not adequately simulate TC–TC interactions as they are observed. Basin-scale HWRF (HWRF-B) was developed later with a multistorm nesting approach to improve the simulation of TC–TC interactions by producing high-resolution forecasts for multiple TCs simultaneously. In this study, the multistorm nesting approach in HWRF-B was compared with a single-storm nesting approach using an otherwise identical model configuration. The multistorm approach demonstrated TC intensity forecast improvements, including more realistic TC–TC interactions. Storm-following nests developed in HWRF and HWRF-B will be foundational to NOAA’s next-generation hurricane application in the Unified Forecast System.

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

The clouds in the Atlantic trade-wind region are known to have an important role in the global climate system. Acquiring a comprehensive characterization of these clouds based on observations is a challenge, but it is a necessary piece of information for the evaluation of their representation in models. An exploration of how the macrophysical and microphysical cloud properties and organization of the cloud field impact the large-scale cloud radiative forcing is presented here. Direct measurements of the cloud radiative effects from the Broadband AirCrAft RaDiometer Instrumentation (BACARDI) on board the High Altitude and LOng Range Research Aircraft (HALO) and cloud observations from the GOES-16 satellite during the Elucidating the role of clouds-circulation coupling in climate (EUREC4A) campaign provide evidence to demonstrate what drives the cloud radiative effects in shallow trade-wind clouds. We find that the solar and terrestrial radiative effects of these clouds are largely driven by their macrophysical properties (cloud fraction and a scene-averaged liquid water path). However, we also conclude that the microphysical properties, cloud top height and the organization of the cloud field demonstrate an increasing relevance in determining the cloud radiative effects as the cloud fraction increases.

Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia–Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016 to 2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy, and high-resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher, but the product accuracy is also higher than that of those products. In particular, hourly and daily root-mean-square errors of the new SWDR are 104.9 and 31.5 W m−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 W m−2, respectively), ERA5 (at 176.6 and 39.5 W m−2, respectively), and GLASS (daily of 36.5 W m−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 W m−2, respectively, which are comparable to that of CERES and ERA5, and even better over high-altitude regions.

The storm surge and hydrodynamics along the Krishna-Godavari (K-G) basin are examined based on numerical experiments designed from assessing the landfalling cyclones in Bay of Bengal (BoB) over the past 38 years with respect to its highest maximum sustained wind speed and its duration. The model is validated with the observed water levels at the tide gauge stations at Visakhapatnam during Helen (2013) and Hudhud (2014). Effect of gradual and rapid intensification of cyclones on the peak water levels and depth average currents are examined and the vulnerable locations are identified. The duration of intensification of a rapidly intensifying cyclone over the continental shelf contributed to about 10-18 % increase in the peak water levels, whereas for the gradually intensifying cyclone the effect is trivial. The inclusion of the wave-setup increased the peak water levels up to 39% compared to those without wave-setup. In the deep water region, only rapidly intensifying cyclones affected the peak MWEs. Intensification over the continental slope region significantly increases the currents along the shelf region and coast. The effect on peak maximum depth averaged current extends up to 400 km from the landfall location. Thus, it is necessary to consider the effect of various combinations of the highest cyclone intensity and duration of intensification for identifying the worst scenarios for impact assessment of coastal processes and sediment transport. The study is quite useful in improving the storm surge prediction, in preparedness, risk evaluation, and vulnerability assessment of the coastal regions in the present changing climate.

During daylight hours, the mean radiant temperature Tmrt$T_{\text{mrt}}$ is one of the most important meteorological parameters to analyse heat stress for humans. This study conducts a spatio-temporal analysis of Tmrt$T_{\text{mrt}}$ for a summer period in 2018 for the city of Berlin, Germany. To this end, the mesoscale climate model COSMO-CLM (CCLM) is coupled with the urban Double Canyon Effect Parameterization scheme with a building energy model (DCEP–BEM) to derive Tmrt$T_{\text{mrt}}$. This coupled model system CCLM/DCEP–BEM enables a dynamic calculation of Tmrt$T_{\text{mrt}}$ for the microscale urban street canyons using a mesoscale model. To bring a more accurate comparison, a two-step approach is applied to assess the radiative fluxes and Tmrt$T_{\text{mrt}}$ from CCLM/DCEP–BEM. The radiation model SOLWEIG is first validated against measurement and then used to evaluate the DCEP–BEM model. Overall good agreement in Tmrt$T_{\text{mrt}}$ is found between CCLM/DCEP–BEM and SOLWEIG (R2=0.96$\text{R}^2 =\nobreak 0.96$). Nighttime Tmrt$T_{\text{mrt}}$ simulated with CCLM/DCEP–BEM is higher than that with SOLWEIG (MBE=2.9K$\text{MBE}=\nobreak 2.9\,\text{K}$), yet closer to measurements. Tmrt$T_{\text{mrt}}$ during the afternoon hours modeled with CCLM/DCEP–BEM is underestimated compared to SOLWEIG (MBE=-3.1K$\text{MBE}=\nobreak -3.1\,\text{K}$). Further, excluding vegetation, higher values for nighttime Tmrt$T_{\text{mrt}}$ are found in the densely built-up city center than in the suburbs with more open structures, while the city center has lower values for Tmrt$T_{\text{mrt}}$ during midday. This study provides a reliable representation of Tmrt$T_{\text{mrt}}$ in a mesoscale model and would be beneficial for future implementation of human-biometeorological variables such as the Universal Thermal Climate Index or Physiological Equivalent Temperature. These quantities are calculated using Tmrt$T_{\text{mrt}}$.

Terrain and building-induced low-level turbulence is studied in this paper. Data from six anemometers, respectively along the North and South Runways of Hong Kong International Airport, are analysed over the period of 2012 to 2018. From this data the recorded turbulence intensities have been computed and the footprints plotted. A joint analysis of the turbulence footprints together with the surrounding environment, using maps and photographs, reveals likely upstream causes influencing the directional distribution of observed turbulence intensities. Signatures from nearby objects and buildings, such as terminals and hangars, are easily identifiable. This paper serves as a unique documentation of possible building influence on wind measurements at a densely-developed, major international airport, and provides useful reference to meteorologists and wind engineers at airports in other parts of the world.

As human exposure to hydroclimatic extremes increase and the number of in situ precipitation observations declines, precipitation estimates, such as those provided by the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) mission, provide a critical source of information. Here, we present a new gauge-enhanced dataset [the Climate Hazards Center IMERG with Stations (CHIMES)] designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center (CHC) high-resolution climatology (CHPclim) and low-latency rain gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES. The CHC’s curated rain gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERGlate fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERGlate performs well (r = 0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50% to 63% and decreases the mean absolute error from 48 to 39 mm month−1. Gauge enhancement then increases the variance explained to 75%, reducing the mean absolute error to 27 mm month−1.

Since 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX), led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic and Meteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain and operate the WP-3D and Gulfstream IV-SP (G-IV) Hurricane Hunter aircraft, and NCEP’s National Hurricane Center and Environmental Modeling Center, who task airborne missions to gather data used by forecasters for analysis and forecasting and for ingest into operational numerical weather prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an integrated approach of analyzing observations from aircraft, initializing and evaluating forecast models with those observations, and developing new airborne instrumentation and observing strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts. This summary article not only highlights recent IFEX contributions toward improved TC understanding and prediction, but also reflects more broadly on the accomplishments of the program during the 16 years of its existence. It describes how IFEX addresses high-priority forecast challenges, summarizes recent collaborations, describes advancements in observing systems monitoring structure and intensity, as well as in assimilation of aircraft data into operational models, and emphasizes key advances in understanding of TC processes, particularly those that lead to rapid intensification. The article concludes by laying the foundation for the next generation of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge inundation, and tornadoes, that have gained notoriety during the last few years after several devastating landfalling TCs.

As part of the analysis following the Seeded and Natural Orographic Wintertime Storms (SNOWIE) project, the ice water content (IWC) in ice and mixed-phase clouds is retrieved from airborne Wyoming Cloud Radar (WCR) measurements aboard the University of Wyoming King Air (UWKA), which has a suite of integrated in situ IWC, optical array probes, and remote sensing measurements, and it provides a unique dataset for this algorithm development and evaluation. A sensitivity study with different idealized ice particle habits shows that the retrieved IWC with aggregate ice particle habit agrees the best with the in situ measurement, especially in ice or ice-dominated mixed-phase clouds with a correlation coefficient (rr) of 0.91 and a bias of close to 0. For mixed-phase clouds with ice fraction ratio less than 0.8, the variances of IWC estimates increase (rr = 0.76) and the retrieved mean IWC is larger than in situ IWC by a factor of 2. This is found to be related to the uncertainty of in situ measurements, the large cloud inhomogeneity, and the retrieval assumption uncertainty. The simulated reflectivity Ze and IWC relationships assuming three idealized ice particle habits and measured particle size distributions show that hexagonal columns with the same Ze have a lower IWC than aggregates, whose Ze–IWC relation is more consistent with the observed WCR Ze and in situ IWC relation in those clouds. The 2D stereo probe (2DS) images also indicate that ice particle habit transition occurs in orographic mixed-phase clouds; hence, the retrieved IWC assuming modified gamma particle size distribution (PSD) of aggregate particles tends to have a greater bias in this kind of clouds.

An ensemble of high‐resolution regional climate model simulation data is used to examine the impacts of climate change on offshore and onshore wind energy generation in Ireland. Two Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 8.5) are analysed for the mid‐term (2041–2060) and the long‐term (2081–2100) future. Wind energy is projected to decrease (≤2%) overall in future climate scenarios. Changes are evident by mid‐century and are more pronounced by late 21st century, particularly for RCP 8.5 offshore. Seasonally, wind energy is projected to decrease by less than 6% in summer and to increase slightly in winter (up to 1.1%). The distinct changes in different parts of the power curve, presented here for the first time, show a reversed pattern of duration at certain levels of the power curve. In summer, there is an increase of low‐power and a decrease of high‐power generation, whereas during winter, there is a projected increase in the time spent at high power. This could lead to diverse consequences for system operators depending on the season. The impacts of climate change on the duration and frequency of long periods (longer than 24 h) of low‐/high‐power wind energy events in Ireland are also presented. The frequency of low‐power events is projected to increase slightly, especially during summer. Onshore and offshore events are considered separately, demonstrating the complementarity of developing both onshore and offshore wind farms for future energy systems. Regional analysis highlights the benefit of developing a geographically dispersed wind farm network incorporating different local wind conditions.

With the advent of phased array radar (PAR) technology, it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than traditional Doppler radar. Research on the assimilation of PAR observations in numerical weather prediction model is still very few in China. In this study, the impact of assimilating PAR data on the model forecasts is investigated with a case study of a local heavy rainfall system that occurred in Foshan city, Guangdong Province on 26 August 2020. A series of sensitive experiments for this study is conducted. Both of the retrieved three-dimensional wind and hydrometeor fields are assimilated through nudging method for the TRAMS_RUC_1km (Tropical Regional Assimilation Model for South China sea_The Rapid Update Cycle_1km). The temperature and moist fields are also adjusted accordingly. The results show that significant improvements are made by the experiments with latent heat nudging and adjustment of moisture vapor field, which implies the importance of thermodynamic balance in the initialization of convective system and highlights the need to assimilate PAR radar observation in a continuous manner to maximize the impact of the data. Sensitivity tests also indicate that the relaxation time should be less than 5 minutes. In general, the assimilation of PAR data can significantly improve the nowcasting of regional heavy precipitation in this case. This paper is the first step toward operational PAR data assimilation in the numerical weather prediction model of southern China.

The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multisource precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered from −1 to 0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1, and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm, Nw). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error. Significance Statement This study first compares the total and different rain types of near-surface rainfall measured by DPR and ground-based radar CDP, then separates the error of DPR near-surface rainfall into systematic and random errors and analyzes the possible causes of the error. The purpose of this study is to better apply the error model to applications such as optimal data fusion and hydrological modeling, and the analysis of the error can also provide a basis for improving the spaceborne precipitation retrieval algorithm.